1. Technical Field
The system is directed to the field of sound processing. More particularly, this system provides a way to remove tonal noise without degrading speech or music.
2. Related Art
Speech enhancement often involves the removal of noise from a speech signal. It has been a challenging topic of research to enhance a speech signal by removing extraneous noise from the signal so that the speech may be recognized by a speech processor or by a listener. Various approaches have been developed over the past decades. Among them the spectral subtraction methods are the most widely used in real-time applications. In this method, an average noise spectrum is estimated and subtracted from the noisy signal spectrum, so that average signal-to-noise ratio (SNR) is improved.
However, prior art speech enhancement techniques do not always work when the noise is of a type referred to as “tonal” noise. Tonal noise can occur in homes, offices, cars, and other environments. An often quoted source of tonal noise in the home and office is the buzzing of fluorescent lights. Another is the hum of a computer or projector fan. In the car tonal noise can result from rumble strips, car engine, alternator whine, radio interference (“GSM buzz”), or a whistle from an open window. This tonal noise can negatively impact phone conversations and speech recognition, making speech a little more difficult to understand or recognize.
A speech processing system which examines an input signal for desired signal content may interpret the tonal noise as speech, may isolate a segment of the input signal with the tonal noise, and may attempt to process the tonal noise. The speech processing system consumes valuable computational resources not only to isolate the segment, but also to process the segment and take action based on the result of the processing. In a speech recognition system, the system may interpret the tonal noise as a voice command, execute the spurious command, and responsively take actions that were never intended.
Tonal noise appears as constant peaks in an acoustic frequency spectrum. By definition the peaks stand out from the broader band noise, often by 6 to 20 dB. Noise reduction typically attenuates all frequencies equally, so the remaining tonal noise is quieter, but is just as distinct after noise reduction as before. Therefore the existing noise removal approach does not really help reduce tonal noise relative to the broader background noise.